1. The introduction about MetaCycle

1.1. General description about MetaCycle

The MetaCycle package is mainly used for detecting rhythmic signals from large scale time-series data. It incorporates ARSER(ARS), JTK_CYCLE(JTK), and Lomb-Scargle(LS) properly for periodic signal detection, and could also output integrated analysis results if required.

1.2. Types of time-series data

The usual time-series data is evenly sampled once at each time point, and the interval between neighbour time points is integer. Not all data are as simple as this. There are datasets with replicate samples, or with missing values, or un-evenly sampled, or sampled with a non-integer interval. Examples of these types of data are shown in the below table.

Of course, some datasets may seem combination of two or more of above types of data.

1.3. Method selection

The meta2d function in MetaCycle is designed to analyze differnt types of time-series datasets, and it could automatically select proper method to analyze different types of input datasets. The implementation strategy used for meta2d is shown in the flow chart.

1.4. Integration strategies

In addition to selecting proper methods to analyze different kinds of datasets, meta2d could also output integrated results from multiple methods. Detail explaination about integration stragegies is in the vignettes of MetaCycle.

• Pvalue

  • Fisher’s method is implemented in meta2d for integrating multiple p-values.

• Period and phase

  • The integrated period is an arithmetic mean value of multiple periods, while phase integration based on mean of circular quantities is implemented in meta2d.

• Amplitude calculation

  • meta2d recalculates the amplitude with following model:

\[Y_i = B + TRE*(t_i - \frac{\sum_{i=1}^n t_i}{n}) + A*cos(2*\pi*\frac{t_i - PHA}{PER})\]

- B is baseline level of the time-series profile
- TRE is trend level of the time-series profile
- A is the amplitude of the waveform
- PHA and PER are integrated period and phase mentioned above. 
- The baseline and trend level are explained in the below example.

• About rAMP

  • meta2d outputs a relative amplitude value (rAMP), which normalizes amplitude values with expression levels. For example, Ugt2b34 has a larger amplitude than Arntl, but its rAMP is smaller than Arntl.

1.5. Description about output values

2. Take a quick check about installed software and packages

• Before the demo, please make sure that you have read the README file for this workshop.

• Check the installed RStudio

  • Click the RStudio icon to open it. The RStudio window will show as below (If the Console part(left bottom) does not shows ‘R version 3.3.0’, plese follow README file for installing the latest R version or switch to the latest R version):

  

• Check the installed R packages

  • Type below command in the Console part to check installed packages

# load 'shiny' package
library("shiny")
# load 'MetaCycle' package
library("MetaCycle")
# load 'dplyr' package
library("dplyr")
# load 'ggplot2' package
library("ggplot2")
# load 'cowplot' package
library("cowplot")

• Check downloaded repositories

  • Type below command to check whether “MetaCycleApp-master”, “PSEA-master” and “SRBR_SMTSAworkshop-master” directories are on the desktop of laptop

# change working directory to the desktop 
setwd("~/Desktop")

# check required directories under the desktop
dir()

• If all required packages are successfully installed and three requied directories has been downloaded to the desktop of labtop, it would show below records in the Console part:

  

Try to open a ‘jar’ file

• Go to “PSEA-master” directory, and could see ‘PSEA1.1_VectorGraphics.jar’

• Right click this file and click ‘open’. On mac computer, it may show below dialog, and just click ‘Open’.

  

• If this ‘jar’ file is successfully opened, you could see below dialog (If not, please follow README file to install latest Java).

  

• Now you could close this ‘jar’ file, and begin to try MetaCycle.

3. The demo pipeline about MetaCycle

3.1. Open the web application of meta2d

• Option 1: use ‘runGitHub’ function by typing below command in the ‘Console’ window of RStudio (this option requires that the laptop is connected with internet)

# load shiny package
library("shiny")

# use 'runGitHub' function of shiny package
runGitHub("MetaCycleApp", "gangwug")

• Option 2: use ‘runApp’ function by typing below command in the ‘Console’ window of RStudio (this option does not require that the laptop is connected with internet)

# load shiny package
library("shiny")

# use 'runApp' function of shiny package
runApp("~/Desktop/MetaCycleApp-master")

• If the web application is successfully opened, it will show a window like below:

  

3.2. Analyze three cases with the web application of meta2d

• caseA (csv): 1) evenly sampled with 4h interval covering two days (from CT18 to CT62), 2) no replicate or missing value, 3) sampling time information is numeric value in the header

  • goal: use JTK_CYCLE to detect circadian transcripts with MetaCycleApp

  • step 1: click ‘Choose File’ button to upload caseA dataset from ‘data’ directory

    

• step 2: set the parameters as shown below (set ‘minper’ and ‘maxper’ to 24 and select ‘JTK’ from ‘cycMethod’) and click ‘Run’ button

  

• step 3: take a quick check of the analysis results

  

• step 4: click ‘download’ button at bottom to download the outputs to ‘result’ directory and name the output file as ‘meta2d_caseA.csv’

  

• caseB (txt): 1) evenly sampled with 6h interval covering two days (from CT20 to CT62), 2) no replicate 3) one missing time point at CT50, 4) sampling time information is non-numeric character in the header

• goal: use JTK_CYCLE and Lomb-Scargle to detect circadian transcripts with MetaCycleApp

• detail steps: 1) follow caseA to upload caseB dataset from ‘data’ directory, 2) parameters setting: select ‘txt’ under fileStyle, paste comma-delimited time values under ‘timepoints’, set ‘minper’ and ‘maxper’ to 24, and select ‘JTK’ and ‘LS’ under ‘cycMethod’, 3) click ‘Run’ button, 4) click ‘Download’ button to save outputs (named as ‘meta2d_caseB.txt’) to ‘result’ directory, 5) you could see a window like below during analyzing caseB dataset

• caseC (csv): 1) cell cycle data from yeast, 2) evenly sampled with 16min interval from 2min to 162min, 3) no replicate or missing value, 4) the expected time length for cell cycle of this kind of yeast is 85min, 5) sampling time information is numeric value in the header

• goal: use ARSER, JTK_CYCLE and Lomb-Scargle to detect cycling transcripts from yeast cell cycle dataset

• detail steps: 1) follow above cases to upload caseC dataset from ‘data’ directory, 2) parameters setting: select ‘csv’ under fileStyle, type ‘Header’ under ‘timepoints’, set ‘minper’ as 80 and ‘maxper’ as 96, set ‘ARSdefaultPer’ as 85, and select all three methods under ‘cycMethod’, 3) click ‘Run’ button, 4) click ‘Download’ button to save outputs (named as ‘meta2d_caseC.csv’) to ‘result’ directory, 5) you could see a window like below during analyzing caseC dataset

  

3.3. Please analyze five exercise datasets with MetaCycleApp according to instructions mentioned above

• general advice:

• 1. five exercise datasets (with ‘exercise’ in the file name) could be found in ‘data’ directory
• 2. select all three methods under ‘cycMethod’ and MetaCycleApp will automaticlly select methods that could be used to analyze input dataset
• 3. for search circadian profiles, it is better to set both ‘minper’ and ‘maxper’ as 24
• 4. select ‘csv’ or ‘txt’ under ‘fileStyle’ according to input file
• 5. sampling time information is non-numeric character in the header of exerciseA, please type comma-deliated time values under ‘timepoints’; for other exercise datasets, type ‘Header’ under ‘timepoints’
• 6. save the outputs in ‘result’ directory with different names

• exerciseA (txt): 1) evenly sampled with 4h interval covering one day (from CT19 to CT39), 2) no replicate or missing value, 3) sampling time information is non-numeric character in the header

• exerciseB (csv): 1) evenly sampled with 4h interval covering two days, 2) three replicates at each time point, 3) no missing value, 4) sampling time information is numeric value in the header

• exerciseC (csv): 1) evenly sampled with 4h interval covering two days, 2) varied number of replicates at each time point, 3) no missing value, 4) sampling time information is numeric value in the header

• exerciseD (csv): 1) evenly sampled with 3h interval covering two days (from CT0 to CT45), 2) three replicates at each time point 3) there is random missing value in some rows, 4) sampling time information is numeric value in the header

• exerciseE (csv): 1) un-evenly sampled from CT19 to CT57, 2) no replicates or missing value, 3) sampling time information is numeric value in the header

3.4. Analyze the experimental data (it may take several minutes)

• experimentA: 1) evenly sampled with 2h interval covering two days (from CT18 to CT64), 2) no replicate or missing value, 3) sampling time information is numeric value in the header, 4) including 15K probesets (about 1/3 of total probesets for MOE4302 array)

• general advice: 1) select ‘JTK’ and ‘LS’ in analyzing high-resolution datasets (eg. 2h / 2days, ‘ARS’ shows relative higher false positive rates in analyzing these kinds of datasets), 2) set ‘minper’ and ‘maxper’ both as ‘24’ for searching circadian transcripts, 3) save outputs in ‘result’ directory and name it as ‘meta2d_experimentA.csv’, 4) if following above advice, you could see a window like below during analyzing this dataset

3.5. Check the expression profiles of circadian transcripts (FDR < 0.05) and show their phase distribution

• Type below command in the ‘Console’ window of RStudio to prepare the dataframe (‘figureD’) used for drawing figures

# load dplyr package
library("dplyr")

# read in the 'meta2d_experientA.csv' file
dataD <- read.csv("~/Desktop/SRBR_SMTSAworkshop-master/result/meta2d_experimentA.csv",
                   stringsAsFactors = FALSE)
# take a look at column names of the data
colnames(dataD)

##  [1] "CycID"         "JTK_pvalue"    "JTK_BH.Q"      "JTK_period"   
##  [5] "JTK_adjphase"  "JTK_amplitude" "LS_pvalue"     "LS_BH.Q"      
##  [9] "LS_period"     "LS_adjphase"   "LS_amplitude"  "meta2d_pvalue"
## [13] "meta2d_BH.Q"   "meta2d_period" "meta2d_phase"  "meta2d_Base"  
## [17] "meta2d_AMP"    "meta2d_rAMP"   "X18"           "X20"          
## [21] "X22"           "X24"           "X26"           "X28"          
## [25] "X30"           "X32"           "X34"           "X36"          
## [29] "X38"           "X40"           "X42"           "X44"          
## [33] "X46"           "X48"           "X50"           "X52"          
## [37] "X54"           "X56"           "X58"           "X60"          
## [41] "X62"           "X64"

# filter the data by meta2d_BH.Q < 0.05 by 'filter' function of dplyr
cirD <- filter(dataD, meta2d_BH.Q < 0.05)
# see the number of circadian transcripts
nrow(cirD)

## [1] 706

# select "CycID", "meta2d_period", "meta2d_phase", and "X18" to "X64" columns 
# for drawing heatmap by 'select' function of dplyr package
figureD <- select(cirD, CycID, meta2d_period, meta2d_phase, 
                   num_range("X", seq(18, 64, by=2), width = 2))

• Type below command in the ‘Console’ window of RStudio to draw a heatmap figure of circadian transcripts (if the code runs well, a heatmap figure will show in the bottom right window of the RStudio)

# load 'heatmapF' and 'phaseHist' function used to draw heatmap and histogram
source("~/Desktop/SRBR_SMTSAworkshop-master/R/fig.R")

# get the heatmap figure by 'heatmapF' function
heatmapFigure <- heatmapF(inputD = figureD, minfold=0.5, maxfold=2)

heatmapFigure

• Type below command in the ‘Console’ window of RStudio to draw a histgram for phase distribution of circadian transcripts selected above (if the code runs well, a histogram figure will show in the bottom right window of the RStudio)

# get the phase distribtuion figure by 'phaseHist' function
histFigure <- phaseHist(inputD = figureD, binvalue=seq(0,24,by=2), histcol = "blue")

histFigure

4. The demo pipeline about PSEA

Phase Set Enrichment Analysis (PSEA) is designed to incorporate prior knowledge into the analysis of periodic data. PSEA could identify biologically related gene sets showing temporally coordinated expression. For more detail introduction about PSEA, please see Ray’s PSEA paper.

4.1. Prepare the file used for PSEA analysis

• Type below command in the ‘Console’ window of RStudio to prepare the file used to PSEA analysis

# take a look at column names of 'cirD' dataframe
colnames(cirD)

##  [1] "CycID"         "JTK_pvalue"    "JTK_BH.Q"      "JTK_period"   
##  [5] "JTK_adjphase"  "JTK_amplitude" "LS_pvalue"     "LS_BH.Q"      
##  [9] "LS_period"     "LS_adjphase"   "LS_amplitude"  "meta2d_pvalue"
## [13] "meta2d_BH.Q"   "meta2d_period" "meta2d_phase"  "meta2d_Base"  
## [17] "meta2d_AMP"    "meta2d_rAMP"   "X18"           "X20"          
## [21] "X22"           "X24"           "X26"           "X28"          
## [25] "X30"           "X32"           "X34"           "X36"          
## [29] "X38"           "X40"           "X42"           "X44"          
## [33] "X46"           "X48"           "X50"           "X52"          
## [37] "X54"           "X56"           "X58"           "X60"          
## [41] "X62"           "X64"

# if it reports error after typing above command, please re-run the code of preparing 
# 'figureD' in the first part of 3.5 section of this demo

# select its 'CycID', "meta2d_BH.Q", 'meta2d_phase' columns for later analysis
phaseD <- select(cirD, CycID, meta2d_BH.Q, meta2d_phase)

# read in the annotation file-'annoDbiMainInfMouse4302.txt' in the 'data-raw' directory
annoD <- read.delim("~/Desktop/SRBR_SMTSAworkshop-master/data-raw/Mouse4302ProbeAnnotation.txt", 
                    stringsAsFactors = FALSE)

# add annotation information with 'inner_join' function of dplyr package
phaseD <- inner_join(phaseD, annoD, by=c("CycID" = "PROBEID"))
# take a look at the phaseD
head(phaseD)

##        CycID  meta2d_BH.Q meta2d_phase ENTREZID  SYMBOL
## 1 1416273_at 6.267205e-04    0.9112647    21928 Tnfaip2
## 2 1418853_at 1.005625e-04    2.2190313    28194    Apon
## 3 1418322_at 1.267709e-03   21.7744648    12916    Crem
## 4 1435748_at 5.230269e-08   16.5923801    14544     Gda
## 5 1448993_at 3.079038e-02   16.0442955    67841    Atg3
## 6 1428889_at 4.627240e-02    4.8168048    69113  Alkbh3

# filter out those probesets without annotation information
phaseD <- filter(phaseD, SYMBOL != "NA")

# select 'SYMBOL', 'meta2d_BH.Q' and 'meta2d_phase' columns for 
# getting a dataframe without duplicate gene names
ori_phaseD <- select(phaseD, SYMBOL, meta2d_BH.Q, meta2d_phase)
# take a look at the row number with possilbe duplicate gene names
dim(ori_phaseD)

## [1] 698   3

# load the 'uniF' function from 'fig.R' for doing this work
source("~/Desktop/SRBR_SMTSAworkshop-master/R/fig.R")
# get a dataframe without duplicate gene names
uni_phaseD <- uniF(ori_phaseD)
# take a look at the rownumber now
dim(uni_phaseD)

## [1] 643   3

# select 'SYMBOL' and 'meta2d_phase' columns for PSEA analysis
pseaD <- select(uni_phaseD, SYMBOL, meta2d_phase)
# take a look at the pseaD
head(pseaD)

##          SYMBOL meta2d_phase
## 1 1110059E24Rik   15.0817948
## 2 1190002N15Rik   15.7701032
## 3 1700023H06Rik    6.0991388
## 4 1810055G02Rik   11.2992141
## 5 2310035C23Rik    1.6562627
## 6 2610507B11Rik    0.8431068

# write the 'pseaD' dataframe to a txt file-'experimentPSEA.txt' in 'result' directory
write.table(pseaD, file = "~/Desktop/SRBR_SMTSAworkshop-master/result/experimentPSEA.txt",
             sep = "\t", quote = FALSE, row.names = FALSE)

• Check the ‘result’ directory and make sure there is a file named ‘experimentPSEA.txt’

4.2. Convert the mouse gene name to its human homolog gene name

• Type below command in the ‘Console’ window of RStudio to convert the mouse gene name to its human homolog gene name

# read in the 'experimentPSEA.txt' file
ori_pseaD <- read.delim("~/Desktop/SRBR_SMTSAworkshop-master/result/experimentPSEA.txt", 
                         stringsAsFactors = FALSE)
# take a look at the data
head(ori_pseaD)

##          SYMBOL meta2d_phase
## 1 1110059E24Rik   15.0817948
## 2 1190002N15Rik   15.7701032
## 3 1700023H06Rik    6.0991388
## 4 1810055G02Rik   11.2992141
## 5 2310035C23Rik    1.6562627
## 6 2610507B11Rik    0.8431068

# read in the 'MouseHumanHomolog.txt' file in 'data-raw' directory of SRBR_SMTSAworkshop-master
homoD <- read.delim("~/Desktop/SRBR_SMTSAworkshop-master/data-raw/MouseHumanHomolog.txt", 
                     stringsAsFactors = FALSE)
# take a look at the data
head(homoD)

##   Mouse_gene Human_gene
## 1      Acadm      ACADM
## 2     Acadvl     ACADVL
## 3      Acat1      ACAT1
## 4      Acvr1      ACVR1
## 5       Sgca       SGCA
## 6       Adsl       ADSL

# join mouse gene and human homolog gene with 'inner_join' function
homo_pseaD <- inner_join(ori_pseaD, homoD, by=c("SYMBOL" = "Mouse_gene"))
# take a look at the joined dataframe
head(homo_pseaD)

##          SYMBOL meta2d_phase Human_gene
## 1 1110059E24Rik   15.0817948    C9orf85
## 2 1190002N15Rik   15.7701032    C3orf58
## 3 1810055G02Rik   11.2992141   C11orf24
## 4 2310035C23Rik    1.6562627   KIAA1468
## 5 2610507B11Rik    0.8431068   KIAA0100
## 6 2700094K13Rik    9.9926117   C11orf31

# select "Human_gene" and "meta2d_phase" columns for PSEA analysis
outD <- select(homo_pseaD, Human_gene, meta2d_phase)
# take a look at the selected data
head(outD)

##   Human_gene meta2d_phase
## 1    C9orf85   15.0817948
## 2    C3orf58   15.7701032
## 3   C11orf24   11.2992141
## 4   KIAA1468    1.6562627
## 5   KIAA0100    0.8431068
## 6   C11orf31    9.9926117

# write it to a file named-'human_experimentPSEA.txt' in 'result' directory of SRBR_SMTSAworkshop-master
write.table(outD, file = "~/Desktop/SRBR_SMTSAworkshop-master/result/human_experimentPSEA.txt", 
              sep = "\t", quote = FALSE, row.names = FALSE, col.names = FALSE)

• Go to ‘result’ directory of SRBR_SMTSAworkshop-master and you will find the output file named as ‘human_experimentPSEA.txt’.

4.3. Do PSEA analysis with ‘PSEA1.1_VectorGraphics.jar’

• Go to ‘PSEA-master’ directory and open ‘PSEA1.1_VectorGraphics.jar’

• Click ‘Browse’ button under ‘STEP 1 Select items file:’ to upload the input file (‘human_experimentPSEA.txt’ in ‘result’ directory of ‘SRBR_SMTSAworkshop-master’) as shown below

• Click ‘Browse’ button under ‘STEP 2 Select sets file:’ to select the biologically related gene sets file (select ‘c2.all.v5.1.symbols.gmt’ file in ‘PSEA-master’ directory) as shown below

• Set corresponding parameters under ‘STEP 3 Select parameters:’ according to the sepecific study (here we use default values, except set q value < 0.1) as shown below

• Click ‘Browse’ button under ‘STEP 4 Select output folder:’ to select the directory used to store output files (select ‘PSEAout’ directory within ‘result’ directory of ‘SRBR_SMTSAworkshop-master’) as shown below

• Click ‘START’ button under ‘STEP 5 Generate output:’ to generate output files and ‘START’ button will change to ‘DONE’ button as shown below

• Go to ‘PSEAout’ directory in ‘result’ directory and you will find three output files (‘vsUniform.svg’, ‘vsBackground.svg’ and ‘results.txt’) and two output directories (‘vsUniform’ and ‘vsBackground’). Open ‘vsUniform.svg’, you will see all phase enriched itmes with significant q value, look like below

• Go to ‘vsUniform’ directory within ‘PSEAout’ directory and you will find gene list corresponding to each enriched item shown on the ‘vsUniform.svg’. There are also figures showing the phase distribution of listed genes for each enriched item, look like below

5. Try your data or share your experience of analyzing time-series data